Inspecting the Solution Space of Genome-Scale Metabolic Models

Genome-scale metabolic models are frequently used in computational biology. They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. However, the huge solution space which comes with the analysis of genome-scale models by using, e.g., Flux Balance Analysis (FBA) poses a problem, since it is hard to thoroughly investigate and often only an arbitrarily selected individual flux distribution is discussed as an outcome of FBA. Here, we introduce a new approach to inspect the solution space and we compare it with other approaches, namely Flux Variability Analysis (FVA) and CoPE-FBA, using several different genome-scale models of lactic acid bacteria. We examine the extent to which different types of experimental data limit the solution space and how the robustness of the system increases as a result. We find that our new approach to inspect the solution space is a good complementary method that offers additional insights into the variance of biological phenotypes and can help to prevent wrong conclusions in the analysis of FBA results.

Inferring kinetic rate constants from single-molecule FRET trajectories – a blind benchmark of kinetic analysis tools

Single-molecule FRET (smFRET) is a versatile technique to study the dynamics and function of biomolecules since it makes nanoscale movements detectable as fluorescence signals. The powerful ability to infer quantitative kinetic information from smFRET data is, however, complicated by experimental limitations. Diverse analysis tools have been developed to overcome these hurdles but a systematic comparison is lacking. Here, we report the results of a blind benchmark study assessing eleven analysis tools used to infer kinetic rate constants from smFRET trajectories. We tested them against simulated and experimental data containing the most prominent difficulties encountered in analyzing smFRET experiments: different noise levels, varied model complexity, non-equilibrium dynamics, and kinetic heterogeneity. Our results highlight the current strengths and limitations in inferring kinetic information from smFRET trajectories. In addition, we formulate concrete recommendations and identify key targets for future developments, aimed to advance our understanding of biomolecular dynamics through quantitative experiment-derived models.

On-grid and in-flow mixing for time-resolved cryo-EM

Time-resolved cryo-electron microscopy (TrEM) allows the study of proteins under non-equilibrium conditions on the millisecond timescale, permitting the analysis of large-scale conformational changes or assembly and disassembly processes. However, the technique is developing and there have been few comparisons with other biochemical kinetic studies. Using current methods, the shortest time delay is on the millisecond timescale (∼5–10 ms), given by the delay between sample application and vitrification, and generating longer time points requires additional approaches such as using a longer delay line between the mixing element and nozzle, or an incubation step on the grid. To compare approaches, the reaction of ATP with the skeletal actomyosin S1 complex was followed on grids prepared with a 7–700 ms delay between mixing and vitrification. Classification of the cryo-EM data allows kinetic information to be derived which agrees with previous biochemical measurements, showing fast dissociation, low occupancy during steady-state hydrolysis and rebinding once ATP has been hydrolysed. However, this rebinding effect is much less pronounced when on-grid mixing is used and may be influenced by interactions with the air–water interface. Moreover, in-flow mixing results in a broader distribution of reaction times due to the range of velocities in a laminar flow profile (temporal spread), especially for longer time delays. This work shows the potential of TrEM, but also highlights challenges and opportunities for further development.

Inferring Numbers of Wild Poliovirus Excretors Using Quantitative Environmental Surveillance

Response to and monitoring of viral outbreaks can be efficiently focused when rapid, quantitative, kinetic information provides the location and the number of infected individuals. Environmental surveillance traditionally provides information on location of populations with contagious, infected individuals since infectious poliovirus is excreted whether infections are asymptomatic or symptomatic. Here, we describe development of rapid (1 week turnaround time, TAT), quantitative RT-PCR of poliovirus RNA extracted directly from concentrated environmental surveillance samples to infer the number of infected individuals excreting poliovirus. The quantitation method was validated using data from vaccination with bivalent oral polio vaccine (bOPV). The method was then applied to infer the weekly number of excreters in a large, sustained, asymptomatic outbreak of wild type 1 poliovirus in Israel (2013) in a population where >90% of the individuals received three doses of inactivated polio vaccine (IPV). Evidence-based intervention strategies were based on the short TAT for direct quantitative detection. Furthermore, a TAT shorter than the duration of poliovirus excretion allowed resampling of infected individuals. Finally, the method documented absence of infections after successful intervention of the asymptomatic outbreak. The methodologies described here can be applied to outbreaks of other excreted viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), where there are (1) significant numbers of asymptomatic infections; (2) long incubation times during which infectious virus is excreted; and (3) limited resources, facilities, and manpower that restrict the number of individuals who can be tested and re-tested.

Drying Kinetics and Modelling of Convective Drying of Kedondong Fruit

Kedondong is an underutilized fruit cultivated in a small scale in Malaysia and it contains nutrients that can be preserved through drying. The dried product can be sold as a premium fruit snack that could generate revenue for the producer. We studied the drying of peeled and unpeeled kedondong fruits using hot air (60-80°C). This study aims to investigate the drying kinetics (drying rates and effective diffusivities) of kedondong fruits and model the drying curves using thin layer models. Ten thin layer models were employed and solved using non-linear regression. Drying kinetics showed that only falling rate periods were observed, which implied that internal diffusion was the dominant mechanism for moisture release. Mathematical models showed that Modified Hii et al. (I) and (II) models were able to predict the drying curve well with the highest R2 (0.9992-0.9999), the lowest RMSE (8.0 x 10-4 - 2.5 x 10-3) and the lowest χ2 (4.0 ×10-5 - 2.0 x 10-4). Peeled samples showed higher effective diffusivities (average 3.2 x 10-11 m2/s) than unpeeled samples (average 2.7 x 10-11 m2/s). The activation energy was lower in peeled samples (25.8 kJ/mol) as moisture diffusion could occur more easily than unpeeled samples (32.1 kJ/mol). Results from this study provide kinetic information that can be used in scaling up of dryer and optimizing dryer performances.

Soft Modelling of the Photolytic Degradation of Moxifloxacin Combining Surface enhanced Raman Spectroscopy and Multivariate Curve Resolution

The antibiotic moxifloxacin had a recent surge in its use due to its broad spectrum of activity. However, due to the low metabolization inside the organism, it became an environmental concern. Here, the photolytic degradation of moxifloxacin antibiotic in alkaline medium was carried out and monitored through SERS spectroscopy. Multivariate curve resolution method was applied to extract quantitative and kinetic information about the whole process, using correlation constraint to simultaneously quantify the variation of moxifloxacin concentration. The results showed that the photolysis follows an apparent first order kinetics with half-life of 47.5 min. Also, SERS spectrum along with the calculated Raman spectra suggest that cleavage of the diazabicyclonyl substituent is the preferred photodegradation pathway, in agreement with previous reports.

Multi-Scale Modeling of Aqueous-Phase Methane Diffusion in Silicate Channels

Silica-based materials including zeolites are commonly used for wide ranging applications including separations and catalysis.<br>Substrate transport rates in these materials often significantly influence the efficiency of such applications.<br>Two factors that contribute to transport rates include<br>1) the porosity of the silicate matrix and<br>2) non-bonding interactions between the diffusing species and the silicate surface.<br>Here, we utilize computer simulation to resolve the relative contribution of these factors to effective methane transport rates in a silicate channel.<br>Specifically, we develop a `homogenized' model of methane transport valid at micron and longer length scales that incorporates atomistic-scale kinetic information.<br>The atomistic-scale data are obtained from extensive molecular dynamics simulations that yield local diffusion coefficients and potentials of mean force.<br>With this model, we demonstrate how nuances in silicate hydration and silica/methane interactions impact 'macroscale' methane diffusion rates in bulk silicate materials.<br>This hybrid homogenization/molecular dynamics approach will be of general use for describing small molecule transport in materials with detailed molecular interactions.<br><br>

Multi-Scale Modeling of Aqueous-Phase Methane Diffusion in Silicate Channels

Silica-based materials including zeolites are commonly used for wide ranging applications including separations and catalysis.<br>Substrate transport rates in these materials often significantly influence the efficiency of such applications.<br>Two factors that contribute to transport rates include<br>1) the porosity of the silicate matrix and<br>2) non-bonding interactions between the diffusing species and the silicate surface.<br>Here, we utilize computer simulation to resolve the relative contribution of these factors to effective methane transport rates in a silicate channel.<br>Specifically, we develop a `homogenized' model of methane transport valid at micron and longer length scales that incorporates atomistic-scale kinetic information.<br>The atomistic-scale data are obtained from extensive molecular dynamics simulations that yield local diffusion coefficients and potentials of mean force.<br>With this model, we demonstrate how nuances in silicate hydration and silica/methane interactions impact 'macroscale' methane diffusion rates in bulk silicate materials.<br>This hybrid homogenization/molecular dynamics approach will be of general use for describing small molecule transport in materials with detailed molecular interactions.<br><br>

Active Knowledge Extraction from Cyclic Voltammetry

<div>Cyclic Voltammetry~(CV) is an electro-chemical characterization technique used in an initial material screening for desired properties and to extract information about electro-chemical reactions.<br></div><div>In some applications, to extract kinetic information of the associated reactions (e.g., rate constants and turn over frequencies), CV curve should have a specific shape (for example an S-shape). </div><div>However, often the settings to obtain such curve are not known \textit{a priori}. </div><div>In this paper, an active search framework is defined to accelerate identification of settings that enable knowledge extraction from CV experiments.</div><div>Towards this goal, a function space representation of CV responses is used in combination with Bayesian Model Selection (BMS) method to efficiently label the response to be either \textit{S-shape} or not \textit{S-shape}. </div><div>Using an active search with BMS oracle, we report a linear target identification in a 6-dimensional design space (comprising of thermodynamic, mass transfer and solution variables as dimensions). </div><div>Our framework has the potential to be a powerful virtual screening technique for molecular catalysts, bi-functional fuel cell catalysts etc.</div>

Active Knowledge Extraction from Cyclic Voltammetry

<div>Cyclic Voltammetry~(CV) is an electro-chemical characterization technique used in an initial material screening for desired properties and to extract information about electro-chemical reactions.<br></div><div>In some applications, to extract kinetic information of the associated reactions (e.g., rate constants and turn over frequencies), CV curve should have a specific shape (for example an S-shape). </div><div>However, often the settings to obtain such curve are not known \textit{a priori}. </div><div>In this paper, an active search framework is defined to accelerate identification of settings that enable knowledge extraction from CV experiments.</div><div>Towards this goal, a function space representation of CV responses is used in combination with Bayesian Model Selection (BMS) method to efficiently label the response to be either \textit{S-shape} or not \textit{S-shape}. </div><div>Using an active search with BMS oracle, we report a linear target identification in a 6-dimensional design space (comprising of thermodynamic, mass transfer and solution variables as dimensions). </div><div>Our framework has the potential to be a powerful virtual screening technique for molecular catalysts, bi-functional fuel cell catalysts etc.</div>